Data clean room

ABSTRACT

Embodiments of the present disclosure may provide a data clean room allowing secure data analysis across multiple accounts, without the use of third parties. Each account may be associated with a different company or party. The data clean room may provide security functions to safeguard sensitive information. For example, the data clean room may restrict access to data in other accounts. The data clean room may also restrict which data may be used in the analysis and may restrict the output. The overlap data may be anonymized to prevent sensitive information from being revealed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/742,721, filed Jan. 14, 2020, titled ELECTRONIC MULTI-TENANT DATAMANAGEMENT SYSTEM, which is incorporated herein by reference in itsentirety.

TECHNICAL FIELD

The present disclosure generally relates to securely analyzing dataacross different accounts using a data clean room.

BACKGROUND

Currently, most digital advertising is performed using third-partycookies. Cookies are small pieces of data generated and sent from a webserver and stored on the user's computer by the user's web browser thatare used to gather data about customers' habits based on their websitebrowsing history. Because of privacy concerns, the use of cookies isbeing restricted.

Companies may want to create target groups for advertising or marketingefforts for specific audience segments. To do so, companies may want tocompare their customer information with that of other companies to seeif their customer lists overlap for the creation of such target groups.Thus, companies may want to perform data analysis, such as an overlapanalysis, of their customers or other data. To perform such types ofdata analyses, companies can use “trusted” third parties, who can accessdata from each of the companies and perform the data analysis. However,this third-party approach suffers from significant disadvantages. First,companies give up control of their customer data to these third parties,which can lead to unforeseen and harmful consequences because this datacan contain sensitive information, such as personal identityinformation. Second, the analysis is performed by the third parties, notthe companies themselves. Thus, the companies have to go back to thethird parties to conduct a more detailed analysis or a differentanalysis. This can increase the expense associated with the analysis aswell as add a time delay. Also, providing such information to thirdparties for this purpose may run afoul of ever-evolving data privacyregulations and common industry policies.

SUMMARY

Embodiments of the present disclosure may provide a data clean roomallowing secure data analysis across multiple accounts, without the useof third parties. Each account may be associated with a differentcompany or party. The data clean room may provide security functions tosafeguard sensitive information. For example, the data clean room mayrestrict access to data in other accounts. The data clean room may alsorestrict which data may be used in the analysis and may restrict theoutput. The overlap data may be anonymized to prevent sensitiveinformation from being revealed.

BRIEF DESCRIPTION OF THE DRAWINGS

Example embodiments will be described and explained with additionalspecificity and details through the use of the accompanying drawings inwhich:

FIG. 1 illustrates an example environment related to an electronicmulti-tenant data management system;

FIG. 2 illustrates an example system related to performing a search inan electronic multi-tenant data management system;

FIGS. 3A-3C illustrate example flow diagrams of an examplecomputer-implemented method to perform a search of an electronicmulti-tenant data management system; and

FIG. 4 illustrates an example computing device that may be used with anelectronic multi-tenant data management system.

DETAILED DESCRIPTION

The following disclosure sets forth numerous specific details such asexamples of specific systems, components, methods, and so forth, inorder to provide a good understanding of several embodiments of thepresent disclosure. It will be apparent to one skilled in the art,however, that at least some embodiments of the present disclosure may bepracticed without these specific details. In other instances, well-knowncomponents or methods are not described in detail or are presented insimple block diagram format in order to avoid unnecessarily obscuringthe present disclosure. Thus, the specific details set forth are merelyexamples. Particular implementations may vary from these example detailsand still be contemplated to be within the scope of the presentdisclosure.

Users generate data across a variety of platforms. Each of theseplatforms may obtain data relative to particular habits and/oractivities of users. For example, web-based shopping sites may obtain ashopping history of a user, a purchase history of a user, a searchhistory of a user, browsing history of a user, and other information. Avideo streaming service may have a viewing history of a user, a searchhistory of a user, customer ratings submitted by the user, and otherinformation. A social media site may have a list of topics, pages,and/or companies that a user has “liked”, subjects and content of postsby a user, a list of topics, pages, and/or companies that a user has“followed”, comments submitted by a user, and other information. Intoday's digital age, users may interact with multiple platforms andservices each day. The multiple platforms and services are typicallyowned and operated by different entities that do not share their datawith others. It may be beneficial for companies to be able to searchdata from multiple different sources to identify a more full picture ofuser activity, identify trends for a user and among multiple users,improve the targeting of advertising for individuals, and/or measure howsuccessful advertising campaigns are, among others.

However, searching and analyzing data across different companies,platforms, and services may be difficult and/or impossible for a varietyof reasons. If user data is not hidden, encrypted or anonymized,companies may be hesitant to share their own data with competitors,particular when the data may help competitors target the companies'customers. For example, a social media site may have little incentive toshare its collection of data about users with a video streaming companyor a web-based shopping site. Additionally, legal restrictions,including privacy regulations, may regulate the dissemination or use ofpersonally identifying information, preventing one company from sharinginformation it gathers with other companies.

Aspects of the present disclosure address these and other shortcomingsof prior systems by improving the sharing of data across computingsystems. The present disclosure provides an electronic multi-tenant datamanagement system that entities can use to cross-share data among otherentities, while still maintaining privacy of user information andcompany proprietary information. Using the electronic multi-tenant datamanagement system, entities can have access to a more full set of dataabout a user and/or a set of users. This increased access may enable thecompanies to provide better electronic data services, such asadvertising, to users. Additionally, electronic multi-tenant datamanagement systems may facilitate the verification of compliance withregulatory restrictions on the sharing and use of information.

FIG. 1 illustrates an example environment 100 in which embodiments ofthe present disclosure can be implemented. The environment 100 mayinclude a network 110, a data provider 1 120A, a data provider 2 120B,and a data provider 3 120C (collectively the data providers 120), a dataaccessor 130, a data enforcer 140, an identity resolution andanonymization service 150, and a data management system 160.

In some embodiments, the network 110 may include a public network (e.g.,the Internet), a private network (e.g., a local area network (LAN) or awide area network (WAN)), a wired network (e.g., an Ethernet network), awireless network (e.g., an 802.11 network, Bluetooth network, or a Wi-Finetwork), a cellular network (e.g., a Long Term Evolution (LTE) orLTE-Advanced network), routers, hubs, switches, server computers, and/ora combination thereof.

Each of the data providers 120, the data accessor 130, the data enforcer140, the identity resolution and anonymization service 150, and the datamanagement system 160 may be or include a computing device such as apersonal computer (PC), a laptop, a server, a mobile phone, a smartphone, a tablet computer, a netbook computer, an e-reader, a personaldigital assistant (PDA), or a cellular phone etc.

Although FIG. 1 depicts three data providers 120, in some embodimentsthe environment 100 may include any number of data providers 120. Thedata providers 120 may be associated with different entities that maygenerate and/or obtain data associated with users. For example, the dataproviders 120 may be associated with video streaming companies,web-based shopping companies, social media companies, search engines,e-commerce companies, and/or other any other type of company. Forexample, the data provider 1 120A may be associated with a videostreaming company and/or platform, the data provider 2 120B may beassociated with a web-based auction company, and the data provider 3120C may be associated with a search engine.

Each of the data providers 120 may be configured to obtain dataassociated with users of services provided by the data providers 120.Continuing the above example, the data provider 1 120A may obtain dataassociated with a variety of customers as the data corpus 1 122A. Thedata corpus 1 122A may include user names, addresses, billinginformation, user preferences, user settings, user search histories,user viewing histories, user ratings, etc. For example, the data corpus1 122A may include a listing of each video streamed by each usertogether with a time when each video was streamed, a location where eachvideo was streamed, a number of times each video was streamed, anyratings submitted by a user associated with any videos streamed by theuser, searches performed by the user, purchases made by the user,language settings of the user including subtitles, captions, languagetracks, and other data of the user. In some embodiments, the data corpus1 122A may correlate data with particular users based on a user's name,user identification, email address, billing information, etc.

Similarly, the data provider 2 120B may obtain data associated with avariety of customers as the data corpus 2 122B. The data corpus 2 122Bmay include similar data as the data corpus 1 122A but may be associatedwith, in this example, a web-based auction company. For example, thedata corpus 2 122B may include a listing of each auction that is beingtracked by each user, each bid and purchase made by each user, productratings submitted by each user relative to purchases made by the user,buyer and/or seller ratings associated with each user, searchesperformed by each user, items each user has listed for sale, a user'sphysical location, etc. In some embodiments, the data corpus 2 122B maycorrelate data with particular users based on a user's name, useridentification, email address, billing information, etc.

Similarly, the data provider 3 120C may obtain data associated with avariety of customers as the data corpus 3 122C. The data corpus 3 122Cmay include similar data as the data corpus 1 122A and the data corpus 2122B but may be associated with, in this example, a search engine. Forexample, the data corpus 3 122C may include a listing of each searchperformed by each user, the sequence of each search performed by eachuser, a timing of each search performed by each user, each search resultthat is examined by each user (e.g. each search result that is opened,read, clicked, etc.), and other data. In some embodiments, the datacorpus 3 122C may correlate data with particular users based on a user'sname, user identification, email address, billing information, etc.

The data corpora 122 may additionally include other information such as,for example, tracked locations of user input (e.g., tracking where auser clicks, where a user moves a mouse, where a user drags a finger ona touchscreen), tracked keystrokes of users, tracked eye movement andeye focus of users, advertisements that are visited by each user,purchase and return history for each user, location of users,demographic information about users such as the users age, ethnicity,education level, income level, gender, etc. and other user data.

In situations in which the systems discussed here collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether programs orfeatures collect user information (e.g., information about a user'ssocial network, social actions, interactions or activities, profession,a user's preferences, a user's viewing history, or a user's currentlocation), or to control whether and/or how to receive content from thecontent server that may be more relevant to the user. In addition,certain data may be treated in one or more ways before it is stored orused, so that personally identifiable information is removed. Forexample, a user's identity may be treated so that no personallyidentifiable information can be determined for the user, or a user'sgeographic location may be generalized where location information isobtained (such as to a city, ZIP code, or state level), so that aparticular location of a user cannot be determined. Thus, the user mayhave control over how information is collected about the user and usedby a content server.

The data corpora 122 may be shared, on a full or limited basis, to thedata management system 160. Each of the data providers 120 may alsoinclude corresponding data rules 124 that dictate how the respectivedata corpus may be shared, used, access, etc. by other data providers120 that can access the data management system. 160. For example, thedata provider 1 120A may include data rules 1 124A, the data provider 2120B may include data rules 2 124B, and the data provider 3 120C mayinclude data rules 3 124C. The data rules 124 may include restrictionson access to the data corpora 122. For example, the data rules 1 124Amay include rules established by the data provider 1 120A for accessingthe data corpus 1 122A. The data rules 1 124A may include a list ofindividuals, corporations, and/or entities who may access the datacorpus 1 122A via the data management system 160. Additionally oralternatively, in some embodiments, the data rules 1 124A may include apermission list which may grant different individuals, corporations,and/or entities different levels of access to the data corpus 1 122A.For example, a first entity may have full access while a second entitymay only have access to a subset of the data corpus 1 122A.

The data rules 1 124A may also include privacy requirements. Forexample, the privacy requirements may include a requirement for aminimum number of user data to be disclosed in response to a searchquery such as a minimum bin aggregation rule. For example, the minimumbin aggregation may be 100 users. The user data may be shared on anindividual basis, or the user data may be aggregated. If a searchresults in fewer than 100 results, the search results of the data corpus1 122A may not be disclosed as the number of search results may notsatisfy the minimum bin aggregation rule. Additionally or alternatively,if the search results in fewer than 100 results, the search results ofthe data corpus 1 122A may not be aggregated and the aggregated data maynot be shared. In at least one embodiment, user data that is shared isanonymized and personally identifiable user information is removedand/or hidden from being identified by data providers other than thedata provider that is sharing the data. In some embodiments, searchresults may need to satisfy multiple data rules 124 such as the datarules 1 124A and the data rules 2 124B. In these and other embodiments,the data rules 1 124A may include a first minimum bin aggregation ruleand the data rules 2 124B may include a second minimum bin aggregationrule. If the first minimum bin aggregation rule is stricter (i.e.,greater) than the second minimum bin aggregation rule, the searchresults may only need to satisfy the first minimum bin aggregation rule.

The data rules 1 124A may also include data transformation rules. Forexample, the data transformation rules may include a requirement forgrouping of search results into bins. For example, in response to asearch query, results from the data corpus 1 122A may be grouped intobins of a particular size and/or the number of search results may berounded to the nearest bin size. When the bin size is 30, the resultsmay be rounded to the nearest 30. Alternatively or additionally, in someembodiments, data transformations may include fuzzing of data. Forexample, rather than providing exact values for data included in thedata corpus 1 122A, the data management system 160 may provide thevalues of the data modified by a relatively small random amount, or datathat has been aggregated.

Each of the data providers 120 may provide its corresponding data corpus122 and data rules 124 to the data management system 160 and may besubject to the respective data rules 124.

The data accessor 130 may be associated with any entity, including thesame or different entities associated with the data providers 120. Insome embodiments, the data accessor 130 may be granted permission toperform searches of one or more the data corpora 122 via the datamanagement system 160. In these and other embodiments, the data accessor130 may be listed as a party that may access the data corpora 122subject to the data rules 124. In some embodiments, the data accessor130 may have access to search some data corpora 122 and may not haveaccess to search other data corpora 122. For example, the data rules 1124A and data rules 2 124B may list the data accessor 130 as an entitythat may perform searches of the data corpus 1 122A and the data corpus2 122B while the data rules 3 124C do not list the data accessor 130 asa permissioned party. Thus, when attempting to perform a search usingthe data management system 160, the search results may not includeresults associated with the data corpus 3 122C.

The data enforcer 140 may be associated with a third-party such as, forexample, a government entity. For example, the data enforcer 140 may beassociated with a regulatory body that works to ensure that datagathered by the data providers 120 and accessed by the data providers120 and/or the data accessor 130 conform to data management requirements146. For example, in some jurisdictions, the data managementrequirements 146 may not permit the gathering of data from minorswithout consent. Alternatively, in some embodiments, the data managementrequirements 146 may not permit targeted advertising to minors or toothers. Additionally or alternatively, in some jurisdictions, datamanagement requirements 146 may not permit the dissemination ofpersonally identifying information by the party that gathered it toother parties. For example, in some jurisdictions, the data managementrequirements 146 may allow the data provider 1 120A to gather personallyidentifying information for use in billing, providing services, etc. butmay not allow the data provider 1 120A to sell or distribute that datato other parties. The data enforcer 140 may use the data managementsystem 160 to verify compliance with the data management requirements146.

The identity resolution and anonymization service 150 may be configuredto obscure and/or remove any personally identifying information of thedata corpora 122 prior to transmittal of the data corpora 122 to thedata management system 160. In some embodiments, the identity resolutionand anonymization service 150 may associate the data of the data corpora122 with an identifier through a process (e.g., a one-way process) suchthat information from two different data corpora 122 associated with aparticular individual may be correlated with each other withoutrevealing the identity of the particular individual. For example, theidentity resolution and anonymization service 150 may anonymize and/orremove from the data corpora 122 names, physical addresses, InternetProtocol (IP) addresses, phone numbers, email addresses, credit records,billing information, etc. In some embodiments, the identity resolutionand anonymization service 150 may anonymize the data corpora 122 suchthat the anonymized identifier of a particular user is the same acrosseach of the data corpora 122 in which the particular user's dataappears. In some embodiments, the identity resolution and anonymizationservice 150 may use a live random access memory (RAM) internalidentification to generate the anonymized identifier.

In some embodiments, the identity resolution and anonymization service150 may attempt to protect personally identifiable information by beingconfigured to act as a service account 152 with restricted access. Forexample, two data providers 120 may desire to share their respectivedata corpora 122 with one another. The two data providers 120 may thenenter into a contract to share data. Responsive to receiving a requestfrom both data providers 120 to create a shared data space 152, theidentity resolution and anonymization service 150 may create the shareddata space 152. The shared data space 152 may be accessed using one ormore of a service account and an encryption key. The shared data space152 may include some or all of the respective data corpora 122 from bothof the data providers 120. Access to the shared data space 152 may berestricted using the service account. Additionally or alternatively,access to the shared data space 152 may be restricted using theencryption key. The encryption key, for example, may limit access onlyto those data providers 120 that have entered into a contract with oneanother. Further, an encryption key may only provide one-way access tothe data providers 120 that have access to the key. Additionally, anencryption key may be generated by Hash-based Message AuthenticationCode (HMAC), Advanced Encryption Standard (AES), Rivest-Shamir-Adleman(RSA), Triple Data Encryption Standard (TripleDES), or any other methodfor encrypting data. Data providers 120 that have an encryption key andaccess to a service account 152 may desire to have additional dataproviders 120 and their data corpora 122 joined to the service account152 In such scenario, a third data provider 120 may be provided anencryption key that grants access to the service account 152 alreadycreated by the first two data providers 120. In at least one embodiment,the encryption key is shared after permission is given by all dataproviders that currently have access to the encryption key.

The data management system 160 may be configured to receive the datacorpora 122 from each of the data providers 120 and correlate the datacorpora 122 with each other as the data corpora 162. In someembodiments, the data management system 160 may obtain the data corpora122 after the identity resolution and anonymization service 150 hasanonymized any personally identifying information from the data corpora122. In some embodiments, the data corpora 162 may include anidentification of the source of the data, i.e. whether a particular datacorpus of the data corpora 162 came from data provider 1 120A, dataprovider 2 120B, and/or data provider 3 120C. The data management system160 may identify and correlate data associated with a user, or a groupof users in the data corpora 162 and store the correlated data as asearchable record or index.

In some embodiments, the data management system 160 may correlate thedata corpora 122 using a non-personally identifying identifier. Forexample, each of the data corpora 122 may include multiple groups ofdata, each group of data associated with a particular non-personallyidentifying identifier. As described above, the non-personallyidentifying identifiers may be generated by the identity resolution andanonymization service 150. The non-personally identifying identifiersmay be generated in such a way that the same non-personally identifyingidentifier is generated for a group of data associated with a particularindividual regardless of whether the group of data is in the data corpus1 122A, the data corpus 2 122B, or the data corpus 3 122C. The datamanagement system 160 may thus correlate the data corpora by identifyinga first group of data in the data corpus 1 122A associated with aparticular non-personally identifying identifier, a second group of datain the data corpus 2 122B associated with the same particularnon-personally identifying identifier, and a third group of data in thedata corpus 3 122C associated with the same particular non-personallyidentifying identifier, and then correlating the first group of datawith the second group of data and the third group of data.

The data management system 160 may be configured to obtain the datarules 124 from each of the data providers 120 as the set of data rules164. In some embodiments, the set of data rules 164 may include anidentification of the source of the data rules, i.e. whether particulardata rules of the set of data rules 164 came from data provider 1 120A,data provider 2 120B, and/or data provider 3 120C.

The data management system 160 may be configured to obtain the datamanagement requirements 146 from the data enforcer 140 as the datamanagement requirements 166.

The data management system 160 may be configured to process, verify,and/or validate search queries received from the data providers 120, thedata accessor 130, and/or the data enforcer 140 to search the datacorpora 162 using the set of data rules 164 and the data managementrequirements 166 as described below relative to FIG. 2. In someembodiments, the data management system 160 may also be configured togrant access to the data enforcer 140 to verify compliance with the datamanagement requirements 166, to verify the contents of the data corpora162.

The data management system 160 may be configured to generate apredictive data model 168 of the data corpora 162. The predictive datamodel 168 may be generated using machine learning and predictiveanalytics on the data corpora 162. For example, a generative adversarialnetwork (GAN) or a privacy-preserving adversarial network (PPAN) may beapplied to the data corpora 162 to generate the predictive data model168 based on the data corpora 162. Additionally, the predictive datamodel 168 may be trained on the real data sets contained in the “virtualclean room” or service account 152, which may limit access to thepredictive data model 168 to those data providers 120 that have anencryption key to the service account 152, and which may restrict dataproviders 120 from creating their own model on the actual data in theservice account 152. The predictive data model 168 may be used for dataproviders 120 to predict behaviors, tendencies, and/or trends related tothe data corpora 162 that is aggregated in the data management system160. The predictive data model 168 may allow an individual data provider120 a more accurate predictive model by combining data corpora 162 frommore than one different data providers 120. Additionally, the predictivedata model 168 may allow the service account 152 to maintain the privacyof the data corpora 162 by not allowing data providers 120 to developtheir own predictive data models on the data corpora 162. For example,data provider 1 120A may provide data corpus 1 122A to a service account152 and data provider 2 120B may provide data corpus 2 122B to the sameservice account 152. A predictive data model 168 may be generated on thecombined data corpora 162 that data provider 1 120A and data provider 2120B have contributed, without disclosing all the data to either of thedata providers 120. The predictive data model 168 may be more accurateand complete than any one data provider 120 could develop on their owndata corpora 122.

Additions, deletions, and modifications may be made to the environment100 of FIG. 1. In some embodiments, the environment 100 may include moreor fewer than three data providers 120. Alternatively or additionally,in some embodiments, the environment 100 may not include a data accessor130 or may include multiple data accessors 130. Alternatively oradditionally, in some embodiments, the data accessor 130 may be the sameentity as one or more of the data providers 120. In some embodiments,the environment 100 may not include the data enforcer 140 or may includemultiple data enforcers 140. For example, in these and otherembodiments, the environment 100 may include multiple data enforcers 140and each data enforcer 140 may correspond with a particular jurisdictionand may include data management requirements 146 associated with theparticular jurisdiction.

In some embodiments, the environment 100 may not include the identityresolution and anonymization service 150. In these and otherembodiments, each data provider 120 may perform its own dataanonymization to remove personally identifying information from itsrespective data corpus 122. Alternatively or additionally, the datamanagement system 160 may perform the removing of personally identifyinginformation from the data corpora 122.

FIG. 2 illustrates an example system 200 related to performing a searchin an electronic multi-tenant data management system. The system 200 maycorrespond with the data management system 160 of FIG. 1. In someembodiments, the system 200 may include a query analyzer 220, a queryrunner 230, a privacy sweep 240, and a result transformer 250.

The query analyzer 220 may include a circuit, code and/or computerinstructions configured to operate the query analyzer 220 to receive asearch query 210 and analyze the search query 210. The search query 210may include a request to search for users of particular services atparticular locations. The search query 210 may include a request tosearch any data corpora such as the data corpora 122 and/or data corpora162 of FIG. 1. For example, the search query 210 may request a search ofthe data corpus associated with a particular data provider, such as thedata corpus 1 122A associated with the data provider 1 120A of FIG. 1.In some embodiments, the query analyzer 220 may analyze the search query210 based a set of data rules, such as the set of data rules 164 of FIG.1, and based on data management requirements, such as the datamanagement requirements 166 of FIG. 1. For example, a data accessor maysubmit a search query 210 that may request that a search be performed ofthe data corpus associated with a particular data provider. However, thedata rules associated with the particular data provider may notauthorize the data accessor to perform searches of the data corpus. Thequery analyzer may thus validate whether the data accessor haspermission to perform a search of the data corpora listed in the searchquery 210.

If the query analyzer 220 determines that the search query 210 is notauthorized, the query analyzer 220 may provide a message to theoriginator of the search query 210 indicating that the search failedand/or was not authorized. If the query analyzer 220 determines that thesearch query 210 is authorized and/or that the data accessor haspermission to perform a search of the data corpora referenced in thesearch query 210, the query analyzer 220 may provide the search query210 to the query runner 230.

The query runner 230 may include a circuit, code and/or computerinstructions configured to operate the query runner 230 to run thesearch query 210. The query runner 230 may perform a search using thesearch query 210 over the associated data corpora. As described, thesearch query 210 may include a list of data corpora to search and a listof terms, locations, data fields, etc. over which to search. The queryrunner 230 may perform the search using the search query 210 and mayobtain search results from the data corpora referenced in the searchquery 210 and may provide the search results to the privacy sweep 240.For example, if the search query 210 includes a particular location anda particular behavior, the query runner 230 may identify all data in thedata corpora that include the particular location and the particularbehavior.

The privacy sweep 240 may include a circuit, code and/or computerinstructions configured to operate the privacy sweep 240. The privacysweep 240 may perform one or more operations on the search results toverify conformance with the data rules and/or data managementrequirements. For example, the number of search results may be lowerthan a required minimum number of search results as set by a dataprovider in a data rule. For example, the search query 210 may requestto search the data corpus of a first data provider and the first dataprovider may have a data rule requiring at least one hundred results. Ifthe number of results of performing a search using the search query 210is less than one hundred, the privacy sweep 240 may indicate that thesearch using the search query 210 has failed the data rules and mayreturn a message indicating that the search failed to the originator ofthe search query 210. In some embodiments, the privacy sweep 240 mayperform multiple sweeps of the results of the search query 210. Forexample, in some embodiments, the privacy sweep 240 may perform a sweepfor each data corpus which was searched using the search query 210.Thus, the privacy sweep 240 may validate the results of the search query210 based on rules associated with each data corpus. For example, thesearch query 210 may request a search of the data corpus from a firstdata provider, the data corpus from a second data provider, and the datacorpus from a third data provider. The privacy sweep 240 may perform afirst sweep of the search results using the data rules of the first dataprovider, a second sweep of the search results using the data rules ofthe second data provider, and a third sweep of the search results usingthe data rules of the third data provider. In some embodiments, theprivacy sweep 240 may also perform a sweep of the search results usingthe data management requirements. For example, the privacy sweep 240 maydetermine whether the search results include any personally identifyinginformation.

If the privacy sweep 240 determines that the search results satisfy allof the relevant data rules and the data management requirements, theprivacy sweep 240 may provide the search results to the resulttransformer 250. In at least one embodiment, failing to satisfy even oneof many rules may cause the privacy sweep 240 to not provide (or notauthorize provision of) the search results. The result transformer 250may perform alterations to the data based on the data rules. Forexample, the data rules associated with one or more data providers mayrequire that the data be fuzzed. As an example of fuzzing the data, asmall and/or random amount may be added or subtracted from at least oneportion of the actual search results. For example, if the data from adata provider include ad exposure data and the data provider has a rulethat ad exposure data must be fuzzed by five minutes, each search resultwith ad exposure data may have a random amount from −5 minutes to +5minutes added to the ad exposure data. Other data may also be fuzzed.For example, ages of individuals may be fuzzed by a year, by two years,or by any number of years. As an additional example of data rules, adata rule may require that data be grouped into buckets of a particularamount. For example, if the search results indicate that 97 userssatisfy the search query and the data rule requires buckets of 30, thesearch results may be transformed to indicate that 90 users satisfy thesearch query. After performing transformations of the search results,the result transformer 250 may output transformed search results 260. Insome embodiments, the result transformer 250 may provide the transformedsearch results 260 to the party that provided the search query 210 tothe query analyzer 220.

Alternatively or additionally, in some embodiments, the transformedsearch results 260 may be used to identify potential targets foradvertising. For example, the transformed search results 260 may includedemographic information, subjects that are “liked” or “favorited”, pastpurchase information, geographic information, frequency of use, andother information that may be used by a company to devise a marketingstrategy. For example, a company may target particular channels, socialmedia sites, and or topics to improve its visibility among segments ofthe population that are more likely to be interested in its products.

Alternatively or additionally, in some embodiments, the transformedsearch results 260 may be used in the creation of new products. Forexample, as described above, the transformed search results 260 mayinclude a viewing history of the movies and/or television shows thatindividuals in a particular demographic have watched. By identifyingcommon movies and/or television shows, a television producer may createa new television series to cater to the particular demographic.

Alternatively or additionally, in some embodiments, the transformedsearch results 260 may be used to identify segments of the populationthat may be at risk for physical and/or emotional disorders. Forexample, the transformed search results 260 may include informationabout individuals with a particular disorder. Using the customer dataassociated with these individuals, a health agency may identifyparticular character traits, interests, purchase histories, streaminghistories, or other details that may correlate with the particulardisorder.

Alternatively or additionally, in some embodiments, the transformedsearch results 260 may be provided to a data enforcer, such as the dataenforcer 140 of FIG. 1. The search query 210 may also be provided to thedata enforcer. In these and other embodiments, the data enforcer mayalso have access to the data corpora. The data enforcer may thus verifythat the data management requirements are being satisfied.

Alternatively or additionally, in some embodiments, the transformedsearch results 260 may be provided to a data provider, such as the dataprovider 1 120A of FIG. 1. The data provider may have access to verifythe search query 210, the data accessor who requested the search query210, and the transformed search results 260.

In some embodiments, the search query 210 may include a request tosearch some data corpora but not others. In these and other embodiments,the query analyzer 220 may only validate the search query 210 based ondata rules associated with the data corpora that the search query 210requests to search. Similarly, the privacy sweep 240 may only performsweeps relative to the data rules associated with the data corpora thatare searched. Similarly, result transformer 250 may only transform thesearch results based on data rules associated with the data corpora thatare searched.

FIGS. 3A-3C illustrate an example flow diagram illustrating a method 300for performing a search of an electronic multi-tenant data managementsystem. The method may be performed by a circuit and/or processing logicthat comprises hardware (e.g., circuitry, dedicated logic, programmablelogic, microcode, etc.), software (e.g., instructions run on a processorto perform hardware simulation), or a combination thereof. Processinglogic can control or interact with one or more devices, applications oruser interfaces, or a combination thereof, to perform operationsdescribed herein. When presenting, receiving or requesting informationfrom a user, processing logic can cause the one or more devices,applications or user interfaces to present information to the user andto receive information from the user.

For simplicity of explanation, the method of FIGS. 3A-3C is illustratedand described as a series of operations. However, acts in accordancewith this disclosure can occur in various orders and/or concurrently andwith other operations not presented and described herein. Further, notall illustrated operations may be required to implement the methods inaccordance with the disclosed subject matter. In addition, those skilledin the art will understand and appreciate that the methods couldalternatively be represented as a series of interrelated states via astate diagram or events.

At block 302, processing logic may obtain a first data corpus from afirst data provider and a second data corpus from a second dataprovider. At block 304, the processing logic may correlate the firstdata corpus with the second data corpus based on a non-personallyidentifying identifier. At block 306, the processing logic may identifya first rule associated with performing searches of the first datacorpus. At block 308, the processing logic may identify a second ruleassociated with performing searches of the second data corpus. At block310, the processing logic may identify a data management requirement.The data management requirement may be associated with a data enforcer.The data enforcer may be different from the first data provider and thesecond data provider.

At block 312, the processing logic may obtain a first search query froma first data accessor to search the first data corpus and the seconddata corpus. At block 314, the processing logic may validate that thefirst data accessor has permission to perform a first search of thefirst data corpus and the second data corpus. At block 316, theprocessing logic may, in response to determining the first data accessorhas permission to perform the first search of the first data corpus andthe second data corpus, obtain first search results from the first datacorpus and the second data corpus based on the first search query. Atblock 318, the processing logic may validate the first search resultsbased on the first rule and the second rule. At block 320, theprocessing logic may validate the first search results based on the datamanagement requirement. At block 322, the processing logic may, inresponse to the first search results satisfying the first rule and thesecond rule, transform the first search results based on the first ruleand the second rule. At block 324, the processing logic may provide thetransformed first search results to the first data accessor in responseto the transformed first search results satisfying the data managementrequirement.

At block 326, the processing logic may provide access to the first datacorpus and the second data corpus to the data enforcer. At block 328,the processing logic may provide the first search query and thetransformed first search results to the data enforcer. At block 330, theprocessing logic may provide access to the first data provider so thatthe first data provider can verify the first search query, the firstdata accessor, and the transformed first search results based on thefirst rule.

At block 332, the processing logic may obtain a third data corpus from athird data provider. At block 334, the processing logic may correlatethe third data corpus with the first data corpus and the second datacorpus based on the non-personally identifying identifier. At block 336,the processing logic may identify a third rule associated withperforming searches of the third data corpus. At block 338, theprocessing logic may obtain a second search query from a second dataaccessor to search the first data corpus and the third data corpus andnot search the second data corpus. At block 340, the processing logicmay validate that the second data accessor has permission to perform asecond search of the first data corpus and the third data corpus.

At block 342, the processing logic may, in response to determining thesecond data accessor has permission to perform the second search of thefirst data corpus and the third data corpus, obtain second searchresults from the first data corpus and the third data corpus based onthe second search query. At block 344, the processing logic may validatethe second search results based on the first rule and the third rule andmay not validate the second search results based on the second rule. Atblock 346, the processing logic may, in response to the second searchresults satisfying the first rule and the third rule, transform thesecond search results based on the first rule and the third rule and maynot transform the second search results based on the second rule. Atblock 348, the processing logic may provide the transformed secondsearch results to the second data accessor.

FIG. 4 illustrates a diagrammatic representation of a machine in theexample form of a computing device 400 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. The computing device400 may be a mobile phone, a smart phone, a netbook computer, arackmount server, a router computer, a server computer, a personalcomputer, a mainframe computer, a laptop computer, a tablet computer, adesktop computer etc., within which a set of instructions, for causingthe machine to perform any one or more of the methodologies discussedherein, may be executed. In alternative embodiments, the machine may beconnected (e.g., networked) to other machines in a LAN, an intranet, anextranet, or the Internet. The machine may operate in the capacity of aserver machine in client-server network environment. The machine may bea PC, a set-top box (STB), a server, a network router, switch or bridge,or any machine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein.

The example computing device 400 includes a processing device (e.g., aprocessor) 402, a main memory 404 (e.g., read-only memory (ROM), flashmemory, dynamic random access memory (DRAM) such as synchronous DRAM(SDRAM)), a static memory 406 (e.g., flash memory, static random accessmemory (SRAM)) and a data storage device 416, which communicate witheach other via a bus 408.

Processing device 402 represents one or more processing devices such asa microprocessor, central processing unit, or the like. Moreparticularly, the processing device 402 may be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,or a processor implementing other instruction sets or processorsimplementing a combination of instruction sets. The processing device402 may also be one or more special-purpose processing devices such asan application specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. The processing device 402 is configured to executeinstructions 426 for performing the operations and steps discussedherein.

The computing device 400 may further include a network interface device422 which may communicate with a network 418. The computing device 400also may include a display device 410 (e.g., a liquid crystal display(LCD) or a cathode ray tube (CRT)), an alphanumeric input device 412(e.g., a keyboard), a cursor control device 414 (e.g., a mouse) and asignal generation device 420 (e.g., a speaker). In one implementation,the display device 410, the alphanumeric input device 412, and thecursor control device 414 may be combined into a single component ordevice (e.g., an LCD touch screen).

The data storage device 416 may include a computer-readable storagemedium 424 on which is stored one or more sets of instructions 426embodying any one or more of the methodologies or functions describedherein. The instructions 426 may also reside, completely or at leastpartially, within the main memory 404 and/or within the processingdevice 402 during execution thereof by the computing device 400, themain memory 404 and the processing device 402 also constitutingcomputer-readable media. The instructions may further be transmitted orreceived over a network 418 via the network interface device 422.

While the computer-readable storage medium 424 is shown in an exampleembodiment to be a single medium, the term “computer-readable storagemedium” should be taken to include a single medium or multiple media(e.g., a centralized or distributed database and/or associated cachesand servers) that store the one or more sets of instructions. The term“computer-readable storage medium” shall also be taken to include anymedium that is capable of storing, encoding or carrying a set ofinstructions for execution by the machine and that cause the machine toperform any one or more of the methodologies of the present disclosure.The term “computer-readable storage medium” shall accordingly be takento include, but not be limited to, solid-state memories, optical mediaand magnetic media.

In the above description, numerous details are set forth. It will beapparent, however, to one of ordinary skill in the art having thebenefit of this disclosure, that embodiments of the disclosure may bepracticed without these specific details. In some instances, well-knownstructures and devices are shown in block diagram form, rather than indetail, in order to avoid obscuring the description.

Some portions of the detailed description are presented in terms ofalgorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared and otherwise manipulated. It has provenconvenient at times, principally for reasons of common usage, to referto these signals as bits, values, elements, symbols, characters, terms,numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the above discussion, itis appreciated that throughout the description, discussions utilizingterms such as “identifying,” “obtaining,” “correlating,” “determining,”“validating,” “receiving,” “generating,” “transforming,” “requesting,”“creating,” “uploading,” “adding,” “presenting,” “removing,”“preventing,” “providing,” or the like, refer to the actions andprocesses of a computer system, or similar electronic computing device,that manipulates and transforms data represented as physical (e.g.,electronic) quantities within the computer system's registers andmemories into other data similarly represented as physical quantitieswithin the computer system memories or registers or other suchinformation storage, transmission or display devices.

Embodiments of the disclosure also relate to an apparatus for performingthe operations herein. This apparatus may be specially constructed forthe required purposes, or it may comprise a computer selectivelyactivated or reconfigured by a computer program stored in the computer.Such a computer program may be stored in a non-transitory computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, compact disc read-only memories(CD-ROMs) and magnetic-optical disks, ROMs, RAMs, erasable programmableread-only memories (EPROMs), electrically erasable programmableread-only memories (EEPROMs), magnetic or optical cards, flash memory,or any type of media suitable for storing electronic instructions.

The words “example” or “exemplary” are used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “example’ or “exemplary” is not necessarily to be construed aspreferred or advantageous over other aspects or designs. Rather, use ofthe words “example” or “exemplary” is intended to present concepts in aconcrete fashion. As used in this application, the term “or” is intendedto mean an inclusive “or” rather than an exclusive “or”. That is, unlessspecified otherwise, or clear from context, “X includes A or B” isintended to mean any of the natural inclusive permutations. That is, ifX includes A; X includes B; or X includes both A and B, then “X includesA or B” is satisfied under any of the foregoing instances. In addition,the articles “a” and “an” as used in this application and the appendedclaims should generally be construed to mean “one or more” unlessspecified otherwise or clear from context to be directed to a singularform. Moreover, use of the term “an embodiment” or “one embodiment” or“an implementation” or “one implementation” throughout is not intendedto mean the same embodiment or implementation unless described as such.Furthermore, the terms “first,” “second,” “third,” “fourth,” etc. asused herein are meant as labels to distinguish among different elementsand may not necessarily have an ordinal meaning according to theirnumerical designation.

The algorithms and displays presented herein are not inherently relatedto any particular computer or other apparatus. Various systems may beused with programs in accordance with the teachings herein, or it mayprove convenient to construct a more specialized apparatus to performthe required method steps. The required structure for a variety of thesesystems will appear from the description below. In addition, the presentdisclosure is not described with reference to any particular programminglanguage. It will be appreciated that a variety of programming languagesmay be used to implement the teachings of the disclosure as describedherein.

The above description sets forth numerous specific details such asexamples of specific systems, components, methods and so forth, in orderto provide a good understanding of several embodiments of the presentdisclosure. It will be apparent to one skilled in the art, however, thatat least some embodiments of the present disclosure may be practicedwithout these specific details. In other instances, well-knowncomponents or methods are not described in detail or are presented insimple block diagram format in order to avoid unnecessarily obscuringthe present disclosure. Thus, the specific details set forth above aremerely examples. Particular implementations may vary from these exampledetails and still be contemplated to be within the scope of the presentdisclosure.

It is to be understood that the above description is intended to beillustrative and not restrictive. Many other embodiments will beapparent to those of skill in the art upon reading and understanding theabove description. The scope of the disclosure should, therefore, bedetermined with reference to the appended claims, along with the fullscope of equivalents to which such claims are entitled.

What is claimed is:
 1. A method comprising: providing a first party dataassociated with a first account in a network-based data system;providing a second party data associated with a second account in thenetwork-based data system; executing, by a processor, a secure functionin view of the first party data and the second party data to generate afirst result; and generating a transformed result in view of the firstresult, the transformed result providing anonymized matches based on thefirst party data and the second party data, the transformed result beingaccessible via the network-based data system for performing analysis ofoverlapping first party and second party data.
 2. The method of claim 1,further comprising restricting the second account from accessing atleast one of the first result or the transformed result.
 3. The methodof claim 1, wherein the first party data includes a first data corpus,wherein the second party data includes a second data corpus, wherein thesecure function is executed using the first data corpus and the seconddata corpus.
 4. The method of claim 1, wherein the secure functionincludes a search query.
 5. The method of claim 4, wherein a searchresults of the search query includes anonymized data.
 6. The method ofclaim 5 further comprising restricting access to the anonymized data. 7.The method of claim 1, wherein the network-based data system includes adistributed data clean room system.
 8. The method of claim 1, furthercomprising receiving a request to join the first party data and thesecond party data.
 9. The method of claim 8, wherein the request to jointhe first party data and the second party data includes a parameter toinfluence how the first party data and the second party data are to bejoined.
 10. The method of claim 1, wherein the transformed resultincludes anonymized data.
 11. A non-transitory machine-storage mediumembodying instructions that, when executed by a machine, cause themachine to perform operations comprising: identify a first party dataassociated with a first account in a network-based data system; identifya second party data associated with a second account in thenetwork-based data system; execute a secure function in view of thefirst party data and the second party data to generate a first result;generate a transformed result in view of the first result, thetransformed result providing anonymized matches based on the first partydata and the second party data, the transformed result being accessiblevia the network-based data system for performing analysis of overlappingfirst party and second party data.
 12. The non-transitorymachine-storage medium of claim 11, the operations further comprising:restricting the second account from accessing at least one of the firstresult or the transformed result.
 13. The non-transitory machine-storagemedium of claim 11, wherein the first party data includes a first datacorpus, wherein the second party data includes a second data corpus,wherein the secure function is executed using the first data corpus andthe second data corpus.
 14. The non-transitory machine-storage medium ofclaim 11, the operations further comprising: restricting access to theanonymized data.
 15. The non-transitory machine-storage medium of claim11, wherein the network-based data system includes a distributed dataclean room system.
 16. The non-transitory machine-storage medium ofclaim 11, the operations further comprising receiving a request to jointhe first party data and the second party data.
 17. The non-transitorymachine-storage medium of claim 16, wherein the request to join thefirst party data and the second party data includes a parameter toinfluence how the first party data and the second party data are to bejoined.
 18. The non-transitory machine-storage medium of claim 11,wherein the transformed result includes anonymized data.
 19. A systemcomprising: one or more processors; and a memory storing instructionsthat, when executed by the one or more processors, cause the system toperform operations comprising: identify a first party data associatedwith a first account in a network-based data system; identify a secondparty data associated with a second account in the network-based datasystem; execute a secure function in view of the first party data andthe second party data to generate a first result; generate a transformedresult in view of the first result, the transformed result providinganonymized matches based on the first party data and the second partydata, the transformed result being accessible via the network-based datasystem for performing analysis of overlapping first party and secondparty data.
 20. The system of claim 19, the operations furthercomprising: restricting the second account from accessing at least oneof the first result or the transformed result.
 21. The system of claim19, wherein the first party data includes a first data corpus, whereinthe second party data includes a second data corpus, wherein the securefunction is executed using the first data corpus and the second datacorpus.
 22. The system of claim 19, wherein the network-based datasystem includes a distributed data clean room system.
 23. The system ofclaim 19, the operations further comprising receiving a request to jointhe first party data and the second party data.
 24. The system of claim23, wherein the request to join the first party data and the secondparty data includes a parameter to influence how the first party dataand the second party data are to be joined.
 25. The system of claim 19,wherein the transformed result includes anonymized data.